Asks for

The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Working command of both frequentist and Bayesian approaches, and the judgment to choose between them for a given decision rather than by default”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Working command of both frequentist and Bayesian approaches, and the judgment to choose between them for a given decision rather than by default”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)”
The ad says“Strong applied experience with time series and web/behavioral data at scale”
The ad says“Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)”
The ad says“Strong applied experience with time series and web/behavioral data at scale”

Values

The ad says“Hands-on experience taking modern inference methods into production (e.g., always-valid p-values, sequential testing, bandit-based allocation)”
The ad says“Hands-on experience taking modern inference methods into production (e.g., always-valid p-values, sequential testing, bandit-based allocation)”
The ad says“Experience with low-traffic or low-power experimentation problems, and with quasi-experimental methods where randomization is constrained (e.g., geo tests, synthetic control, difference-in-differences, switchback designs)”
The ad says“Experience with low-traffic or low-power experimentation problems, and with quasi-experimental methods where randomization is constrained (e.g., geo tests, synthetic control, difference-in-differences, switchback designs)”
The ad says“Experience with interference, network effects, or two-sided marketplace experimentation”
The ad says“Experience with evaluation of ML or LLM systems (e.g., offline/online evaluation design, metric development)”
The ad says“Experience with evaluation of ML or LLM systems (e.g., offline/online evaluation design, metric development)”
The ad says“Experience with interference, network effects, or two-sided marketplace experimentation”
The ad says“Hands-on experience taking modern inference methods into production (e.g., always-valid p-values, sequential testing, bandit-based allocation)”
The ad says“Experience with low-traffic or low-power experimentation problems, and with quasi-experimental methods where randomization is constrained (e.g., geo tests, synthetic control, difference-in-differences, switchback designs)”
The ad says“Experience with low-traffic or low-power experimentation problems, and with quasi-experimental methods where randomization is constrained (e.g., geo tests, synthetic control, difference-in-differences, switchback designs)”
The ad says“Experience with interference, network effects, or two-sided marketplace experimentation”

About G2 - The Company

G2 is the world's largest and most trusted software marketplace. When you join G2, you’re joining the industry’s leading team that helps businesses reach their peak potential by powering decisions and strategies with trusted insights from real software users.

Now, we have joined forces with Capterra, SoftwareAdvice, and GetApp to create the largest source of online data and software insights to fuel intelligent buying in the age of AI. With 200M+ combined annual visitors and 6M verified reviews, we are now the centralized place to enable software buyers to make better and faster decisions with confidence.

And we are just getting started! We are setting out to transform the global B2B software industry and become the most trusted data foundation for buyers and sellers of software for the age of AI.

Does that sound exciting to you? Come join us as we try to reach our next PEAK!

About G2 - Our People

At G2, everything we are and what we do is grounded in our PEAK values— (Performance + Entrepreneurship + Authenticity + Kindness. Working at G2 means you are part of a value-driven, growing global community that climbs PEAKs together. We cheer for each other’s successes, learn from our mistakes, and support and lean on one another during challenging times. With ambition and entrepreneurial spirit we push each other to take on challenging work, which will help us all to grow and learn.

You will be part of a global, diverse team of smart, dedicated, and kind individuals - each with unique talents, aspirations, and life experiences. At the heart of our community and culture are our people-led ERGs, which celebrate and highlight the diverse identities of our global team. As an organization, we are intentional about our DEI and philanthropic work (like our G2 Gives program) because it encourages us all to be better people.

About The Role

Summary of Responsibilities:

The Data Science team sits within G2's Product org, supporting Product Leadership. As the principal data scientist, you'll help shape how experimentation is done, and how to use data science to inform product decisions.

Your ultimate goal is to play matchmaker: connecting software vendors with the right buyers through G2’s product offerings, including ads, reviews, and agentic evaluation. As G2 expands into the agentic world, you'll partner with stakeholders to modernize existing products and develop new ones for a rapidly evolving, AEO-driven (answer engine optimization), privacy-constrained landscape.

This is a senior individual contributor role: you'll spend your time as a hands-on individual contributor, building models, designing auctions, and shipping production systems. Being a senior technical leader, you are also responsible for mentoring your team and evangelizing the data science best practices across the company . We're looking for someone who wants to stay deep in technical work, with the ability to up-level others in the organization.

In This Role, You Will:

Own the experimentation standard for G2 and G2 Digital Markets. Consolidate the practices currently spread across properties into a single, documented, defensible approach covering experiment design, randomization, metric definition, guardrails, sample size and duration, and how results get read and acted on.
Influence engineering and product to adopt it. Standards that live in a doc do not change outcomes. You will work directly with engineering teams on instrumentation, assignment, and platform behavior, and make the correct approach the low-friction one — through tooling, templates, review, and consistent partnership rather than mandate.
Define the requirements for holistic experimentation at G2. Determine what our experimentation capability needs to look like end to end — platform requirements, telemetry, metric layers, analysis tooling, review process, and the organizational habits around them — and make the case for it to product and engineering leadership.
Design methods that fit our actual traffic and iteration constraints. Build a portfolio of approaches suited to different volume regimes and decision speeds (e.g., high-throughput testing where traffic is abundant, variance reduction and sensitivity techniques where it is not, sound alternatives where a randomized test is not feasible at all).
Drive the exploration of modern experimentation methods. Evaluate emerging approaches and operationalize the ones that earn their place, with a considered point of view on when each is and is not the right tool (e.g., Bayesian decision frameworks, always-valid inference and sequential testing, multi-armed bandits, variance reduction methods, interference-aware designs for marketplace settings).
Evangelize data science and make its impact legible. Teach the practice across product, engineering, and business stakeholders; raise the quality of how experiment results are interpreted and communicated; and hold the organization to an honest account of what our tests actually demonstrate.

Minimum Qualifications:

We realize applying for jobs can feel daunting at times. Even if you don’t check all the boxes in the job description, we encourage you to apply anyway.

8+ years of relevant experience in data science, applied statistics, or a closely related field, with a substantial portion focused on experimentation; or a PhD in a quantitative discipline plus 6+ years
Deep expertise in experimental design and causal inference, including a working knowledge of the failure modes of online controlled experiments (e.g., power and sensitivity analysis, variance reduction, multiple testing correction, heterogeneous treatment effects)
Working command of both frequentist and Bayesian approaches, and the judgment to choose between them for a given decision rather than by default
Demonstrated experience designing experimentation methodology for a real product — not only analyzing tests, but deciding how they should be run
A track record of setting technical standards that other teams actually adopted, and of influencing engineering partners without authority over them
Strong applied experience with time series and web/behavioral data at scale
Fluency in Python and SQL, and comfort in modern data ecosystems (e.g., Snowflake, Spark, Airflow, dbt)
Exceptional written and verbal communication, including the ability to explain a methodological trade-off to a non-technical stakeholder and defend it to a skeptical technical one

What Can Help Your Application Stand Out:

10+ years of relevant experience, including ownership of experimentation practice or platform at a company running experiments across multiple products or properties
Hands-on experience taking modern inference methods into production (e.g., always-valid p-values, sequential testing, bandit-based allocation)
Experience with low-traffic or low-power experimentation problems, and with quasi-experimental methods where randomization is constrained (e.g., geo tests, synthetic control, difference-in-differences, switchback designs)
Experience with interference, network effects, or two-sided marketplace experimentation
Experience serving as a technical lead — setting direction for a group, mentoring senior data scientists, reviewing others' designs and analyses
Experience with evaluation of ML or LLM systems (e.g., offline/online evaluation design, metric development)
Contributions to the experimentation community through publications, open-source work, or conference participation
PhD in a quantitative discipline or MS in STEM

Our Commitment to Inclusivity and Diversity

At G2, we are committed to creating an inclusive and diverse environment where people of every background can thrive and feel welcome. We consider applicants without regard to race, color, creed, religion, national origin, genetic information, gender identity or expression, sexual orientation, pregnancy, age, or marital, veteran, or physical or mental disability status. Learn more about our commitments here.

For job applicants in California, the United Kingdom, and the European Union, please review this applicant privacy notice before applying to this job.

How We Use AI Technology in Our Hiring Process
G2 incorporates AI-powered technology to enhance our candidate evaluation process. These tools may assist with initial application screening, skills assessment analysis, and identifying candidates whose qualifications align with specific role requirements. While AI technology supports our recruitment workflow, all final hiring decisions remain under human oversight and judgment.

Your Choice Matters: If you would prefer that your application be reviewed without AI assistance, you can opt out by entering your email address in the email entry field at the bottom of the Automated Processing Legal Notice. Choosing to opt out will not disadvantage your application in any way—we will ensure your materials receive a thorough manual review by our hiring team.
For additional details about how we handle your information throughout the application process, please review G2's Applicant Privacy Notice.